Certification Professional-Data-Engineer Cost, Test Professional-Data-Engineer Objectives Pdf

Certification Professional-Data-Engineer Cost, Test Professional-Data-Engineer Objectives Pdf

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Google Professional-Data-Engineer certification is a highly sought-after certification that is designed for data professionals who are interested in demonstrating their proficiency in designing, building, and managing data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is intended for data engineers, data analysts, and data scientists who have experience working with data processing systems and are looking to advance their careers.

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Google Certified Professional Data Engineer Exam Sample Questions (Q254-Q259):

You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?

  • A. Create a Cloud Dataproc Workflow Template
  • B. Create an initialization action to execute the jobs
  • C. Create a Directed Acyclic Graph in Cloud Composer
  • D. Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster

Answer: C

Case Study: 2 – MJTelco
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost. Their management and operations teams are situated all around the globe creating many-to- many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments ?development/test, staging, and production ?
to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community. Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
Provide reliable and timely access to data for analysis from distributed research workers Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud’s machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
MJTelco is building a custom interface to share data. They have these requirements:
They need to do aggregations over their petabyte-scale datasets. They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

  • A. Cloud Bigtable and Cloud SQL
  • B. Cloud Datastore and Cloud Bigtable
  • C. BigQuery and Cloud Storage
  • D. BigQuery and Cloud Bigtable

Answer: D

Which of these is not a supported method of putting data into a partitioned table?

  • A. Use ORDER BY to put a table’s rows into chronological order and then change the table’s type to “Partitioned”.
  • B. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
  • C. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format “$YYYYMMDD”.
  • D. Create a partitioned table and stream new records to it every day.

Answer: A

You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using “$YYYYMMDD” at the end of the table name.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

  • A. Use the TABLE_DATE_RANGE function
  • B. Use the WHERE_PARTITIONTIME pseudo column
  • C. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD

Answer: A

A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time.
This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

  • A. Implement clustering in BigQuery on the ingest date column.
  • B. Tier older data onto Cloud Storage files, and leverage extended tables.
  • C. Re-create the table using data partitioning on the package delivery date.
  • D. Implement clustering in BigQuery on the package-tracking ID column.

Answer: D


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